Overview

Dataset statistics

Number of variables51
Number of observations39717
Missing cells118497
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 MiB
Average record size in memory408.0 B

Variable types

Numeric25
Categorical12
Text7
DateTime5
Boolean2

Alerts

pymnt_plan has constant value ""Constant
initial_list_status has constant value ""Constant
application_type has constant value ""Constant
loan_status is highly imbalanced (51.4%)Imbalance
pub_rec is highly imbalanced (86.6%)Imbalance
pub_rec_bankruptcies is highly imbalanced (83.7%)Imbalance
emp_title has 2459 (6.2%) missing valuesMissing
emp_length has 1075 (2.7%) missing valuesMissing
desc has 12942 (32.6%) missing valuesMissing
mths_since_last_delinq has 25682 (64.7%) missing valuesMissing
mths_since_last_record has 36931 (93.0%) missing valuesMissing
next_pymnt_d has 38577 (97.1%) missing valuesMissing
pub_rec_bankruptcies has 697 (1.8%) missing valuesMissing
annual_inc is highly skewed (γ1 = 30.9491846)Skewed
collection_recovery_fee is highly skewed (γ1 = 25.02941576)Skewed
id has unique valuesUnique
member_id has unique valuesUnique
url has unique valuesUnique
delinq_2yrs has 35405 (89.1%) zerosZeros
inq_last_6mths has 19300 (48.6%) zerosZeros
mths_since_last_delinq has 443 (1.1%) zerosZeros
mths_since_last_record has 670 (1.7%) zerosZeros
revol_bal has 994 (2.5%) zerosZeros
out_prncp has 38577 (97.1%) zerosZeros
out_prncp_inv has 38577 (97.1%) zerosZeros
total_rec_late_fee has 37671 (94.8%) zerosZeros
recoveries has 35499 (89.4%) zerosZeros
collection_recovery_fee has 35935 (90.5%) zerosZeros

Reproduction

Analysis started2024-04-10 11:29:49.607329
Analysis finished2024-04-10 11:31:33.009795
Duration1 minute and 43.4 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683131.91
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:33.127929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile372418.4
Q1516221
median665665
Q3837755
95-th percentile1039966.2
Maximum1077501
Range1022767
Interquartile range (IQR)321534

Descriptive statistics

Standard deviation210694.13
Coefficient of variation (CV)0.30842379
Kurtosis-0.7298894
Mean683131.91
Median Absolute Deviation (MAD)160026
Skewness0.078807632
Sum2.713195 × 1010
Variance4.4392018 × 1010
MonotonicityNot monotonic
2024-04-10T17:01:33.312889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69001 1
 
< 0.1%
588642 1
 
< 0.1%
583126 1
 
< 0.1%
587559 1
 
< 0.1%
588668 1
 
< 0.1%
588657 1
 
< 0.1%
588664 1
 
< 0.1%
588649 1
 
< 0.1%
588646 1
 
< 0.1%
588608 1
 
< 0.1%
Other values (39707) 39707
> 99.9%
ValueCountFrequency (%)
54734 1
< 0.1%
55742 1
< 0.1%
57245 1
< 0.1%
57416 1
< 0.1%
58915 1
< 0.1%
59006 1
< 0.1%
61390 1
< 0.1%
61419 1
< 0.1%
62102 1
< 0.1%
65426 1
< 0.1%
ValueCountFrequency (%)
1077501 1
< 0.1%
1077430 1
< 0.1%
1077175 1
< 0.1%
1076863 1
< 0.1%
1075358 1
< 0.1%
1075269 1
< 0.1%
1072053 1
< 0.1%
1071795 1
< 0.1%
1071570 1
< 0.1%
1070078 1
< 0.1%

member_id
Real number (ℝ)

UNIQUE 

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean850463.56
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:33.481815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile388192.4
Q1666780
median850812
Q31047339
95-th percentile1269461.8
Maximum1314167
Range1243468
Interquartile range (IQR)380559

Descriptive statistics

Standard deviation265678.31
Coefficient of variation (CV)0.31239235
Kurtosis-0.56296801
Mean850463.56
Median Absolute Deviation (MAD)190427
Skewness-0.21241637
Sum3.3777861 × 1010
Variance7.0584963 × 1010
MonotonicityNot monotonic
2024-04-10T17:01:33.666668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265533 1
 
< 0.1%
756244 1
 
< 0.1%
749343 1
 
< 0.1%
754885 1
 
< 0.1%
756270 1
 
< 0.1%
756259 1
 
< 0.1%
756267 1
 
< 0.1%
756242 1
 
< 0.1%
756248 1
 
< 0.1%
756206 1
 
< 0.1%
Other values (39707) 39707
> 99.9%
ValueCountFrequency (%)
70699 1
< 0.1%
73673 1
< 0.1%
74724 1
< 0.1%
76583 1
< 0.1%
80353 1
< 0.1%
80364 1
< 0.1%
84914 1
< 0.1%
85483 1
< 0.1%
86999 1
< 0.1%
89243 1
< 0.1%
ValueCountFrequency (%)
1314167 1
< 0.1%
1313524 1
< 0.1%
1311748 1
< 0.1%
1311441 1
< 0.1%
1306957 1
< 0.1%
1306721 1
< 0.1%
1305201 1
< 0.1%
1305008 1
< 0.1%
1304956 1
< 0.1%
1304884 1
< 0.1%

loan_amnt
Real number (ℝ)

Distinct885
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11219.444
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:33.845060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7456.6707
Coefficient of variation (CV)0.66462035
Kurtosis0.76866855
Mean11219.444
Median Absolute Deviation (MAD)5000
Skewness1.0593173
Sum4.4560265 × 108
Variance55601938
MonotonicityNot monotonic
2024-04-10T17:01:34.024812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2833
 
7.1%
12000 2334
 
5.9%
5000 2051
 
5.2%
6000 1908
 
4.8%
15000 1895
 
4.8%
20000 1626
 
4.1%
8000 1586
 
4.0%
25000 1390
 
3.5%
4000 1130
 
2.8%
3000 1030
 
2.6%
Other values (875) 21934
55.2%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 301
0.8%
1050 4
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 679
1.7%
34800 2
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475 5
 
< 0.1%
34200 1
 
< 0.1%
34000 15
 
< 0.1%
33950 9
 
< 0.1%
33600 6
 
< 0.1%
33500 2
 
< 0.1%

funded_amnt
Real number (ℝ)

Distinct1041
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10947.713
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:34.199046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15400
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation7187.2387
Coefficient of variation (CV)0.65650593
Kurtosis0.93755199
Mean10947.713
Median Absolute Deviation (MAD)4600
Skewness1.0817102
Sum4.3481032 × 108
Variance51656400
MonotonicityNot monotonic
2024-04-10T17:01:34.383974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2741
 
6.9%
12000 2244
 
5.6%
5000 2040
 
5.1%
6000 1898
 
4.8%
15000 1784
 
4.5%
8000 1573
 
4.0%
20000 1456
 
3.7%
25000 1133
 
2.9%
4000 1127
 
2.8%
3000 1022
 
2.6%
Other values (1031) 22699
57.2%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 302
0.8%
1050 5
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 554
1.4%
34800 1
 
< 0.1%
34675 2
 
< 0.1%
34525 1
 
< 0.1%
34475 4
 
< 0.1%
34250 1
 
< 0.1%
34000 14
 
< 0.1%
33950 6
 
< 0.1%
33600 6
 
< 0.1%
33500 1
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

Distinct7940
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10397.449
Minimum0
Maximum35000
Zeros142
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:34.557035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1873.658
Q15000
median8975
Q314400
95-th percentile24736.572
Maximum35000
Range35000
Interquartile range (IQR)9400

Descriptive statistics

Standard deviation7128.4504
Coefficient of variation (CV)0.6855961
Kurtosis1.0625444
Mean10397.449
Median Absolute Deviation (MAD)4200
Skewness1.1062129
Sum4.1295548 × 108
Variance50814806
MonotonicityNot monotonic
2024-04-10T17:01:34.731066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1309
 
3.3%
10000 1275
 
3.2%
6000 1200
 
3.0%
12000 1069
 
2.7%
8000 900
 
2.3%
4000 813
 
2.0%
3000 804
 
2.0%
15000 659
 
1.7%
7000 600
 
1.5%
2000 453
 
1.1%
Other values (7930) 30635
77.1%
ValueCountFrequency (%)
0 142
0.4%
0.01 7
 
< 0.1%
0.48 1
 
< 0.1%
12 1
 
< 0.1%
18.04 1
 
< 0.1%
23.99 1
 
< 0.1%
25 1
 
< 0.1%
32.33 1
 
< 0.1%
42.81 1
 
< 0.1%
50.34 1
 
< 0.1%
ValueCountFrequency (%)
35000 135
0.3%
34997.35 1
 
< 0.1%
34993.66 1
 
< 0.1%
34993.33 1
 
< 0.1%
34993.26 1
 
< 0.1%
34993.2 1
 
< 0.1%
34990.43 1
 
< 0.1%
34987.98 1
 
< 0.1%
34987.27 1
 
< 0.1%
34977.35 1
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
36 months
29096 
60 months
10621 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 36 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 29096
73.3%
60 months 10621
 
26.7%

Length

2024-04-10T17:01:34.904449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:35.030894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
months 39717
50.0%
36 29096
36.6%
60 10621
 
13.4%

Most occurring characters

ValueCountFrequency (%)
79434
20.0%
6 39717
10.0%
m 39717
10.0%
o 39717
10.0%
n 39717
10.0%
t 39717
10.0%
h 39717
10.0%
s 39717
10.0%
3 29096
 
7.3%
0 10621
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 238302
60.0%
Space Separator 79434
 
20.0%
Decimal Number 79434
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 39717
16.7%
o 39717
16.7%
n 39717
16.7%
t 39717
16.7%
h 39717
16.7%
s 39717
16.7%
Decimal Number
ValueCountFrequency (%)
6 39717
50.0%
3 29096
36.6%
0 10621
 
13.4%
Space Separator
ValueCountFrequency (%)
79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 238302
60.0%
Common 158868
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 39717
16.7%
o 39717
16.7%
n 39717
16.7%
t 39717
16.7%
h 39717
16.7%
s 39717
16.7%
Common
ValueCountFrequency (%)
79434
50.0%
6 39717
25.0%
3 29096
 
18.3%
0 10621
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 397170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79434
20.0%
6 39717
10.0%
m 39717
10.0%
o 39717
10.0%
n 39717
10.0%
t 39717
10.0%
h 39717
10.0%
s 39717
10.0%
3 29096
 
7.3%
0 10621
 
2.7%
Distinct371
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:35.333312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6942871
Min length5

Characters and Unicode

Total characters226160
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st row8.94%
2nd row14.26%
3rd row11.14%
4th row13.17%
5th row8.00%
ValueCountFrequency (%)
10.99 956
 
2.4%
13.49 826
 
2.1%
11.49 825
 
2.1%
7.51 787
 
2.0%
7.88 725
 
1.8%
7.49 656
 
1.7%
11.71 607
 
1.5%
9.99 603
 
1.5%
7.90 582
 
1.5%
5.42 573
 
1.4%
Other values (361) 32577
82.0%
2024-04-10T17:01:36.010760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 39717
17.6%
% 39717
17.6%
1 38195
16.9%
9 21893
9.7%
2 12734
 
5.6%
7 12132
 
5.4%
6 12033
 
5.3%
4 11091
 
4.9%
5 9947
 
4.4%
3 9929
 
4.4%
Other values (2) 18772
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146726
64.9%
Other Punctuation 79434
35.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38195
26.0%
9 21893
14.9%
2 12734
 
8.7%
7 12132
 
8.3%
6 12033
 
8.2%
4 11091
 
7.6%
5 9947
 
6.8%
3 9929
 
6.8%
8 9527
 
6.5%
0 9245
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 39717
50.0%
% 39717
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 226160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 39717
17.6%
% 39717
17.6%
1 38195
16.9%
9 21893
9.7%
2 12734
 
5.6%
7 12132
 
5.4%
6 12033
 
5.3%
4 11091
 
4.9%
5 9947
 
4.4%
3 9929
 
4.4%
Other values (2) 18772
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 39717
17.6%
% 39717
17.6%
1 38195
16.9%
9 21893
9.7%
2 12734
 
5.6%
7 12132
 
5.4%
6 12033
 
5.3%
4 11091
 
4.9%
5 9947
 
4.4%
3 9929
 
4.4%
Other values (2) 18772
8.3%

installment
Real number (ℝ)

Distinct15383
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.56192
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:36.256769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile71.246
Q1167.02
median280.22
Q3430.78
95-th percentile762.996
Maximum1305.19
Range1289.5
Interquartile range (IQR)263.76

Descriptive statistics

Standard deviation208.87487
Coefficient of variation (CV)0.64355939
Kurtosis1.2468013
Mean324.56192
Median Absolute Deviation (MAD)123.2
Skewness1.1284191
Sum12890626
Variance43628.713
MonotonicityNot monotonic
2024-04-10T17:01:36.479606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.11 68
 
0.2%
180.96 59
 
0.1%
311.02 54
 
0.1%
150.8 48
 
0.1%
368.45 46
 
0.1%
372.12 45
 
0.1%
330.76 43
 
0.1%
339.31 42
 
0.1%
301.6 41
 
0.1%
317.72 41
 
0.1%
Other values (15373) 39230
98.8%
ValueCountFrequency (%)
15.69 1
< 0.1%
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
19.87 1
< 0.1%
20.22 1
< 0.1%
21.25 1
< 0.1%
21.74 1
< 0.1%
21.81 1
< 0.1%
ValueCountFrequency (%)
1305.19 1
 
< 0.1%
1302.69 1
 
< 0.1%
1295.21 1
 
< 0.1%
1288.1 2
 
< 0.1%
1283.5 1
 
< 0.1%
1276.6 3
< 0.1%
1272.2 1
 
< 0.1%
1269.73 5
< 0.1%
1265.16 1
 
< 0.1%
1263.23 1
 
< 0.1%

grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B
12020 
A
10085 
C
8098 
D
5307 
E
2842 
Other values (2)
1365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowC
3rd rowB
4th rowD
5th rowA

Common Values

ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Length

2024-04-10T17:01:36.679722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:36.846134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 12020
30.3%
a 10085
25.4%
c 8098
20.4%
d 5307
13.4%
e 2842
 
7.2%
f 1049
 
2.6%
g 316
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 39717
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 39717
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%

sub_grade
Categorical

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
B3
2917 
A4
2886 
A5
2742 
B5
2704 
B4
 
2512
Other values (30)
25956 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA5
2nd rowC5
3rd rowB1
4th rowD2
5th rowA3

Common Values

ValueCountFrequency (%)
B3 2917
 
7.3%
A4 2886
 
7.3%
A5 2742
 
6.9%
B5 2704
 
6.8%
B4 2512
 
6.3%
C1 2136
 
5.4%
B2 2057
 
5.2%
C2 2011
 
5.1%
B1 1830
 
4.6%
A3 1810
 
4.6%
Other values (25) 16112
40.6%

Length

2024-04-10T17:01:37.046877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b3 2917
 
7.3%
a4 2886
 
7.3%
a5 2742
 
6.9%
b5 2704
 
6.8%
b4 2512
 
6.3%
c1 2136
 
5.4%
b2 2057
 
5.2%
c2 2011
 
5.1%
b1 1830
 
4.6%
a3 1810
 
4.6%
Other values (25) 16112
40.6%

Most occurring characters

ValueCountFrequency (%)
B 12020
15.1%
A 10085
12.7%
4 8293
10.4%
3 8215
10.3%
C 8098
10.2%
5 8070
10.2%
2 7907
10.0%
1 7232
9.1%
D 5307
6.7%
E 2842
 
3.6%
Other values (2) 1365
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 39717
50.0%
Decimal Number 39717
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 8293
20.9%
3 8215
20.7%
5 8070
20.3%
2 7907
19.9%
1 7232
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 39717
50.0%
Common 39717
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 12020
30.3%
A 10085
25.4%
C 8098
20.4%
D 5307
13.4%
E 2842
 
7.2%
F 1049
 
2.6%
G 316
 
0.8%
Common
ValueCountFrequency (%)
4 8293
20.9%
3 8215
20.7%
5 8070
20.3%
2 7907
19.9%
1 7232
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 12020
15.1%
A 10085
12.7%
4 8293
10.4%
3 8215
10.3%
C 8098
10.2%
5 8070
10.2%
2 7907
10.0%
1 7232
9.1%
D 5307
6.7%
E 2842
 
3.6%
Other values (2) 1365
 
1.7%

emp_title
Text

MISSING 

Distinct28820
Distinct (%)77.4%
Missing2459
Missing (%)6.2%
Memory size310.4 KiB
2024-04-10T17:01:37.296080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length78
Median length55
Mean length18.379784
Min length2

Characters and Unicode

Total characters684794
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25641 ?
Unique (%)68.8%

Sample

1st rowInfotrieve, Inc.
2nd rowUBS
3rd rowkmex/univision
4th rowGAP
5th rowState of Michigan
ValueCountFrequency (%)
inc 3197
 
3.2%
of 3008
 
3.0%
1208
 
1.2%
and 963
 
1.0%
center 818
 
0.8%
bank 805
 
0.8%
county 803
 
0.8%
services 795
 
0.8%
school 750
 
0.7%
the 747
 
0.7%
Other values (18882) 87491
87.0%
2024-04-10T17:01:37.812890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64766
 
9.5%
e 55954
 
8.2%
a 43836
 
6.4%
n 42641
 
6.2%
o 42586
 
6.2%
i 40491
 
5.9%
r 40067
 
5.9%
t 38580
 
5.6%
s 30254
 
4.4%
l 25923
 
3.8%
Other values (86) 259696
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 489338
71.5%
Uppercase Letter 119545
 
17.5%
Space Separator 64766
 
9.5%
Other Punctuation 8798
 
1.3%
Dash Punctuation 1031
 
0.2%
Decimal Number 968
 
0.1%
Open Punctuation 159
 
< 0.1%
Close Punctuation 156
 
< 0.1%
Math Symbol 21
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 14579
 
12.2%
S 13325
 
11.1%
A 8885
 
7.4%
I 7566
 
6.3%
M 6518
 
5.5%
P 6077
 
5.1%
T 5691
 
4.8%
L 5561
 
4.7%
E 5241
 
4.4%
D 5056
 
4.2%
Other values (18) 41046
34.3%
Lowercase Letter
ValueCountFrequency (%)
e 55954
11.4%
a 43836
9.0%
n 42641
8.7%
o 42586
8.7%
i 40491
 
8.3%
r 40067
 
8.2%
t 38580
 
7.9%
s 30254
 
6.2%
l 25923
 
5.3%
c 23099
 
4.7%
Other values (17) 105907
21.6%
Other Punctuation
ValueCountFrequency (%)
. 4253
48.3%
, 2194
24.9%
& 1301
 
14.8%
' 652
 
7.4%
/ 311
 
3.5%
# 36
 
0.4%
@ 10
 
0.1%
: 9
 
0.1%
! 8
 
0.1%
" 8
 
0.1%
Other values (5) 16
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 192
19.8%
2 161
16.6%
3 155
16.0%
0 98
10.1%
4 91
9.4%
5 72
 
7.4%
9 62
 
6.4%
6 58
 
6.0%
7 46
 
4.8%
8 33
 
3.4%
Math Symbol
ValueCountFrequency (%)
+ 18
85.7%
| 2
 
9.5%
< 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 158
99.4%
[ 1
 
0.6%
Control
ValueCountFrequency (%)
€ 1
50.0%
ƒ 1
50.0%
Currency Symbol
ValueCountFrequency (%)
¢ 1
50.0%
$ 1
50.0%
Space Separator
ValueCountFrequency (%)
64766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1031
100.0%
Close Punctuation
ValueCountFrequency (%)
) 156
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Symbol
ValueCountFrequency (%)
© 2
100.0%
Other Number
ValueCountFrequency (%)
² 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 608883
88.9%
Common 75911
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 55954
 
9.2%
a 43836
 
7.2%
n 42641
 
7.0%
o 42586
 
7.0%
i 40491
 
6.7%
r 40067
 
6.6%
t 38580
 
6.3%
s 30254
 
5.0%
l 25923
 
4.3%
c 23099
 
3.8%
Other values (45) 225452
37.0%
Common
ValueCountFrequency (%)
64766
85.3%
. 4253
 
5.6%
, 2194
 
2.9%
& 1301
 
1.7%
- 1031
 
1.4%
' 652
 
0.9%
/ 311
 
0.4%
1 192
 
0.3%
2 161
 
0.2%
( 158
 
0.2%
Other values (31) 892
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 684780
> 99.9%
None 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64766
 
9.5%
e 55954
 
8.2%
a 43836
 
6.4%
n 42641
 
6.2%
o 42586
 
6.2%
i 40491
 
5.9%
r 40067
 
5.9%
t 38580
 
5.6%
s 30254
 
4.4%
l 25923
 
3.8%
Other values (77) 259682
37.9%
None
ValueCountFrequency (%)
à 3
21.4%
© 2
14.3%
² 2
14.3%
 2
14.3%
€ 1
 
7.1%
¢ 1
 
7.1%
â 1
 
7.1%
ƒ 1
 
7.1%
¡ 1
 
7.1%

emp_length
Categorical

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing1075
Missing (%)2.7%
Memory size310.4 KiB
10+ years
8879 
< 1 year
4583 
2 years
4388 
3 years
4095 
4 years
3436 
Other values (6)
13261 

Length

Max length9
Median length7
Mean length7.4943067
Min length6

Characters and Unicode

Total characters289595
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row< 1 year
2nd row3 years
3rd row< 1 year
4th row10+ years
5th row< 1 year

Common Values

ValueCountFrequency (%)
10+ years 8879
22.4%
< 1 year 4583
11.5%
2 years 4388
11.0%
3 years 4095
10.3%
4 years 3436
 
8.7%
5 years 3282
 
8.3%
1 year 3240
 
8.2%
6 years 2229
 
5.6%
7 years 1773
 
4.5%
8 years 1479
 
3.7%

Length

2024-04-10T17:01:38.009019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 30819
37.6%
10 8879
 
10.8%
1 7823
 
9.6%
year 7823
 
9.6%
4583
 
5.6%
2 4388
 
5.4%
3 4095
 
5.0%
4 3436
 
4.2%
5 3282
 
4.0%
6 2229
 
2.7%
Other values (3) 4510
 
5.5%

Most occurring characters

ValueCountFrequency (%)
43225
14.9%
y 38642
13.3%
e 38642
13.3%
a 38642
13.3%
r 38642
13.3%
s 30819
10.6%
1 16702
 
5.8%
0 8879
 
3.1%
+ 8879
 
3.1%
< 4583
 
1.6%
Other values (8) 21940
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185387
64.0%
Decimal Number 47521
 
16.4%
Space Separator 43225
 
14.9%
Math Symbol 13462
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16702
35.1%
0 8879
18.7%
2 4388
 
9.2%
3 4095
 
8.6%
4 3436
 
7.2%
5 3282
 
6.9%
6 2229
 
4.7%
7 1773
 
3.7%
8 1479
 
3.1%
9 1258
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
y 38642
20.8%
e 38642
20.8%
a 38642
20.8%
r 38642
20.8%
s 30819
16.6%
Math Symbol
ValueCountFrequency (%)
+ 8879
66.0%
< 4583
34.0%
Space Separator
ValueCountFrequency (%)
43225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185387
64.0%
Common 104208
36.0%

Most frequent character per script

Common
ValueCountFrequency (%)
43225
41.5%
1 16702
 
16.0%
0 8879
 
8.5%
+ 8879
 
8.5%
< 4583
 
4.4%
2 4388
 
4.2%
3 4095
 
3.9%
4 3436
 
3.3%
5 3282
 
3.1%
6 2229
 
2.1%
Other values (3) 4510
 
4.3%
Latin
ValueCountFrequency (%)
y 38642
20.8%
e 38642
20.8%
a 38642
20.8%
r 38642
20.8%
s 30819
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43225
14.9%
y 38642
13.3%
e 38642
13.3%
a 38642
13.3%
r 38642
13.3%
s 30819
10.6%
1 16702
 
5.8%
0 8879
 
3.1%
+ 8879
 
3.1%
< 4583
 
1.6%
Other values (8) 21940
7.6%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
RENT
18899 
MORTGAGE
17659 
OWN
3058 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length5
Mean length5.7039555
Min length3

Characters and Unicode

Total characters226544
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowMORTGAGE
3rd rowMORTGAGE
4th rowRENT
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT 18899
47.6%
MORTGAGE 17659
44.5%
OWN 3058
 
7.7%
OTHER 98
 
0.2%
NONE 3
 
< 0.1%

Length

2024-04-10T17:01:38.144591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:38.297533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
rent 18899
47.6%
mortgage 17659
44.5%
own 3058
 
7.7%
other 98
 
0.2%
none 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 226544
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 226544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 36659
16.2%
R 36656
16.2%
T 36656
16.2%
G 35318
15.6%
N 21963
9.7%
O 20818
9.2%
M 17659
7.8%
A 17659
7.8%
W 3058
 
1.3%
H 98
 
< 0.1%

annual_inc
Real number (ℝ)

SKEWED 

Distinct5318
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68968.926
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:38.467031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140404
median59000
Q382300
95-th percentile142000
Maximum6000000
Range5996000
Interquartile range (IQR)41896

Descriptive statistics

Standard deviation63793.766
Coefficient of variation (CV)0.92496388
Kurtosis2302.7378
Mean68968.926
Median Absolute Deviation (MAD)20000
Skewness30.949185
Sum2.7392388 × 109
Variance4.0696446 × 109
MonotonicityNot monotonic
2024-04-10T17:01:38.645079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1505
 
3.8%
50000 1057
 
2.7%
40000 876
 
2.2%
45000 830
 
2.1%
30000 825
 
2.1%
75000 811
 
2.0%
65000 803
 
2.0%
70000 733
 
1.8%
48000 723
 
1.8%
80000 662
 
1.7%
Other values (5308) 30892
77.8%
ValueCountFrequency (%)
4000 1
 
< 0.1%
4080 1
 
< 0.1%
4200 2
 
< 0.1%
4800 4
< 0.1%
4888 1
 
< 0.1%
5000 1
 
< 0.1%
5500 1
 
< 0.1%
6000 5
< 0.1%
7000 1
 
< 0.1%
7200 4
< 0.1%
ValueCountFrequency (%)
6000000 1
 
< 0.1%
3900000 1
 
< 0.1%
2039784 1
 
< 0.1%
1900000 1
 
< 0.1%
1782000 1
 
< 0.1%
1440000 1
 
< 0.1%
1362000 1
 
< 0.1%
1250000 1
 
< 0.1%
1200000 4
< 0.1%
1176000 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Not Verified
16921 
Verified
12809 
Source Verified
9987 

Length

Max length15
Median length12
Mean length11.464335
Min length8

Characters and Unicode

Total characters455329
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowNot Verified
3rd rowNot Verified
4th rowVerified
5th rowNot Verified

Common Values

ValueCountFrequency (%)
Not Verified 16921
42.6%
Verified 12809
32.3%
Source Verified 9987
25.1%

Length

2024-04-10T17:01:38.825628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:38.968082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
verified 39717
59.6%
not 16921
25.4%
source 9987
 
15.0%

Most occurring characters

ValueCountFrequency (%)
e 89421
19.6%
i 79434
17.4%
r 49704
10.9%
V 39717
8.7%
f 39717
8.7%
d 39717
8.7%
o 26908
 
5.9%
26908
 
5.9%
N 16921
 
3.7%
t 16921
 
3.7%
Other values (3) 29961
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 361796
79.5%
Uppercase Letter 66625
 
14.6%
Space Separator 26908
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 89421
24.7%
i 79434
22.0%
r 49704
13.7%
f 39717
11.0%
d 39717
11.0%
o 26908
 
7.4%
t 16921
 
4.7%
u 9987
 
2.8%
c 9987
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V 39717
59.6%
N 16921
25.4%
S 9987
 
15.0%
Space Separator
ValueCountFrequency (%)
26908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428421
94.1%
Common 26908
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 89421
20.9%
i 79434
18.5%
r 49704
11.6%
V 39717
9.3%
f 39717
9.3%
d 39717
9.3%
o 26908
 
6.3%
N 16921
 
3.9%
t 16921
 
3.9%
S 9987
 
2.3%
Other values (2) 19974
 
4.7%
Common
ValueCountFrequency (%)
26908
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 89421
19.6%
i 79434
17.4%
r 49704
10.9%
V 39717
8.7%
f 39717
8.7%
d 39717
8.7%
o 26908
 
5.9%
26908
 
5.9%
N 16921
 
3.7%
t 16921
 
3.7%
Other values (3) 29961
 
6.6%
Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Minimum2007-01-06 00:00:00
Maximum2011-01-12 00:00:00
2024-04-10T17:01:39.130885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:39.372067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

loan_status
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Fully Paid
32950 
Charged Off
5627 
Current
 
1140

Length

Max length11
Median length10
Mean length10.055568
Min length7

Characters and Unicode

Total characters399377
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowCharged Off
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 32950
83.0%
Charged Off 5627
 
14.2%
Current 1140
 
2.9%

Length

2024-04-10T17:01:39.547115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:39.704754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
fully 32950
42.1%
paid 32950
42.1%
charged 5627
 
7.2%
off 5627
 
7.2%
current 1140
 
1.5%

Most occurring characters

ValueCountFrequency (%)
l 65900
16.5%
38577
9.7%
a 38577
9.7%
d 38577
9.7%
u 34090
8.5%
F 32950
8.3%
y 32950
8.3%
P 32950
8.3%
i 32950
8.3%
f 11254
 
2.8%
Other values (8) 40602
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 282506
70.7%
Uppercase Letter 78294
 
19.6%
Space Separator 38577
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 65900
23.3%
a 38577
13.7%
d 38577
13.7%
u 34090
12.1%
y 32950
11.7%
i 32950
11.7%
f 11254
 
4.0%
r 7907
 
2.8%
e 6767
 
2.4%
g 5627
 
2.0%
Other values (3) 7907
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
F 32950
42.1%
P 32950
42.1%
C 6767
 
8.6%
O 5627
 
7.2%
Space Separator
ValueCountFrequency (%)
38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360800
90.3%
Common 38577
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 65900
18.3%
a 38577
10.7%
d 38577
10.7%
u 34090
9.4%
F 32950
9.1%
y 32950
9.1%
P 32950
9.1%
i 32950
9.1%
f 11254
 
3.1%
r 7907
 
2.2%
Other values (7) 32695
9.1%
Common
ValueCountFrequency (%)
38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 399377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 65900
16.5%
38577
9.7%
a 38577
9.7%
d 38577
9.7%
u 34090
8.5%
F 32950
8.3%
y 32950
8.3%
P 32950
8.3%
i 32950
8.3%
f 11254
 
2.8%
Other values (8) 40602
10.2%

pymnt_plan
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
False
39717 
ValueCountFrequency (%)
False 39717
100.0%
2024-04-10T17:01:39.841072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

url
Text

UNIQUE 

Distinct39717
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:40.086116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length64
Median length63
Mean length63.108367
Min length62

Characters and Unicode

Total characters2506475
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39717 ?
Unique (%)100.0%

Sample

1st rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=69001
2nd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=59006
3rd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=65426
4th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=68926
5th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=69251
ValueCountFrequency (%)
https://lendingclub.com/browse/loandetail.action?loan_id=69001 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=69924 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=281384 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=281565 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=281651 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=65426 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=68926 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=69251 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=65640 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=69828 1
 
< 0.1%
Other values (39707) 39707
> 99.9%
2024-04-10T17:01:40.546094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 198585
 
7.9%
l 198585
 
7.9%
n 198585
 
7.9%
a 158868
 
6.3%
t 158868
 
6.3%
/ 158868
 
6.3%
i 158868
 
6.3%
c 119151
 
4.8%
e 119151
 
4.8%
. 79434
 
3.2%
Other values (25) 957512
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1826982
72.9%
Other Punctuation 317736
 
12.7%
Decimal Number 242606
 
9.7%
Uppercase Letter 39717
 
1.6%
Connector Punctuation 39717
 
1.6%
Math Symbol 39717
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 198585
10.9%
l 198585
10.9%
n 198585
10.9%
a 158868
8.7%
t 158868
8.7%
i 158868
8.7%
c 119151
 
6.5%
e 119151
 
6.5%
b 79434
 
4.3%
d 79434
 
4.3%
Other values (8) 357453
19.6%
Decimal Number
ValueCountFrequency (%)
5 26616
11.0%
6 26607
11.0%
7 26037
10.7%
8 25774
10.6%
4 25584
10.5%
1 24160
10.0%
0 23856
9.8%
3 22052
9.1%
9 21694
8.9%
2 20226
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 158868
50.0%
. 79434
25.0%
? 39717
 
12.5%
: 39717
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 39717
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 39717
100.0%
Math Symbol
ValueCountFrequency (%)
= 39717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1866699
74.5%
Common 639776
 
25.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 198585
10.6%
l 198585
10.6%
n 198585
10.6%
a 158868
 
8.5%
t 158868
 
8.5%
i 158868
 
8.5%
c 119151
 
6.4%
e 119151
 
6.4%
b 79434
 
4.3%
d 79434
 
4.3%
Other values (9) 397170
21.3%
Common
ValueCountFrequency (%)
/ 158868
24.8%
. 79434
12.4%
? 39717
 
6.2%
_ 39717
 
6.2%
= 39717
 
6.2%
: 39717
 
6.2%
5 26616
 
4.2%
6 26607
 
4.2%
7 26037
 
4.1%
8 25774
 
4.0%
Other values (6) 137572
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2506475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 198585
 
7.9%
l 198585
 
7.9%
n 198585
 
7.9%
a 158868
 
6.3%
t 158868
 
6.3%
/ 158868
 
6.3%
i 158868
 
6.3%
c 119151
 
4.8%
e 119151
 
4.8%
. 79434
 
3.2%
Other values (25) 957512
38.2%

desc
Text

MISSING 

Distinct26526
Distinct (%)99.1%
Missing12942
Missing (%)32.6%
Memory size310.4 KiB
2024-04-10T17:01:40.895345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3988
Median length2248
Mean length426.5256
Min length1

Characters and Unicode

Total characters11420223
Distinct characters142
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26499 ?
Unique (%)99.0%

Sample

1st rowTaking advantage of excellent credit to pay off credit card
2nd rowI am seeking to refinance a credit account which I closed with a balance when I rejected the new terms of the cardmember agreement. This closed account is adversely affecting my credit utilization percentage and I would prefer to move it to a fixed-rate loan. I am a software developer who has been in a stable position with the same company since 2004. I am up-to-date on all payments and am seeking only to reduce the interest rate of this debt. Thank you for your consideration.
3rd rowWe currently have one car that is 19 years old and one that is 8 years old. The 19 year old car, which is the car my husband drives to his job at a local university, was just given about a month to live by our mechanic. We've gotten an amazing amount of use out of it but we will need to be sure to get a reliable vehicle before that one gives out. We hope to be able to donate it with some life left in it to a local non-profit. That is what we have done in the past with our old cars. Our mechanic will help us find a used car in great shape for around $10,000. We have saved about half of that but we really need to make a purchase soon. It would be fabulous to get a loan for a lower percentage rate than what our credit union offers. Currently that is probably about 11% for older vehicles. Thanks for considering us.
4th rowI need a loan to cover moving expenses such as buying new furniture, deposit on the apt etc.
5th rowLooking to pay bills with a lower rate and try a new type of lending. Please note my perfect credit history and ability to pay the account. Many Thanks Heather
ValueCountFrequency (%)
i 77512
 
3.8%
to 71096
 
3.5%
a 54855
 
2.7%
the 54340
 
2.7%
and 54329
 
2.6%
my 51308
 
2.5%
on 49132
 
2.4%
37238
 
1.8%
for 32774
 
1.6%
have 32490
 
1.6%
Other values (53986) 1535184
74.9%
2024-04-10T17:01:41.457461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2121185
18.6%
e 953962
 
8.4%
a 714029
 
6.3%
o 709011
 
6.2%
t 649103
 
5.7%
n 612135
 
5.4%
r 589058
 
5.2%
i 496003
 
4.3%
s 426272
 
3.7%
d 397984
 
3.5%
Other values (132) 3751481
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8135698
71.2%
Space Separator 2121258
 
18.6%
Decimal Number 346663
 
3.0%
Other Punctuation 326744
 
2.9%
Uppercase Letter 302770
 
2.7%
Math Symbol 140645
 
1.2%
Currency Symbol 16745
 
0.1%
Dash Punctuation 13032
 
0.1%
Close Punctuation 7337
 
0.1%
Open Punctuation 6727
 
0.1%
Other values (7) 2604
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 94559
31.2%
B 34778
 
11.5%
T 28474
 
9.4%
A 15522
 
5.1%
C 14303
 
4.7%
M 14262
 
4.7%
S 9642
 
3.2%
E 9211
 
3.0%
W 8830
 
2.9%
L 8656
 
2.9%
Other values (21) 64533
21.3%
Lowercase Letter
ValueCountFrequency (%)
e 953962
11.7%
a 714029
 
8.8%
o 709011
 
8.7%
t 649103
 
8.0%
n 612135
 
7.5%
r 589058
 
7.2%
i 496003
 
6.1%
s 426272
 
5.2%
d 397984
 
4.9%
l 355636
 
4.4%
Other values (18) 2232505
27.4%
Other Punctuation
ValueCountFrequency (%)
. 120658
36.9%
/ 116441
35.6%
, 50144
15.3%
' 13317
 
4.1%
! 6738
 
2.1%
% 5704
 
1.7%
: 5281
 
1.6%
; 3357
 
1.0%
& 2616
 
0.8%
" 801
 
0.2%
Other values (10) 1687
 
0.5%
Control
ValueCountFrequency (%)
1287
60.5%
€ 411
 
19.3%
™ 191
 
9.0%
’ 38
 
1.8%
“ 37
 
1.7%
‚ 35
 
1.6%
ƒ 27
 
1.3%
 27
 
1.3%
œ 23
 
1.1%
š 15
 
0.7%
Other values (9) 37
 
1.7%
Decimal Number
ValueCountFrequency (%)
0 104184
30.1%
1 97487
28.1%
2 36710
 
10.6%
5 21465
 
6.2%
3 17828
 
5.1%
9 16291
 
4.7%
4 13709
 
4.0%
6 13182
 
3.8%
7 12927
 
3.7%
8 12880
 
3.7%
Math Symbol
ValueCountFrequency (%)
> 84845
60.3%
< 53870
38.3%
+ 984
 
0.7%
= 615
 
0.4%
~ 290
 
0.2%
¬ 31
 
< 0.1%
| 10
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
¦ 96
83.5%
© 15
 
13.0%
2
 
1.7%
® 2
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 13017
99.9%
9
 
0.1%
6
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 7290
99.4%
] 44
 
0.6%
} 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 6681
99.3%
[ 44
 
0.7%
{ 2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 13
68.4%
^ 5
 
26.3%
¯ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
2121185
> 99.9%
  73
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 16671
99.6%
¢ 74
 
0.4%
Final Punctuation
ValueCountFrequency (%)
78
81.2%
18
 
18.8%
Initial Punctuation
ValueCountFrequency (%)
18
85.7%
3
 
14.3%
Other Number
ValueCountFrequency (%)
½ 6
75.0%
¾ 2
 
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8438468
73.9%
Common 2981755
 
26.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2121185
71.1%
. 120658
 
4.0%
/ 116441
 
3.9%
0 104184
 
3.5%
1 97487
 
3.3%
> 84845
 
2.8%
< 53870
 
1.8%
, 50144
 
1.7%
2 36710
 
1.2%
5 21465
 
0.7%
Other values (73) 174766
 
5.9%
Latin
ValueCountFrequency (%)
e 953962
 
11.3%
a 714029
 
8.5%
o 709011
 
8.4%
t 649103
 
7.7%
n 612135
 
7.3%
r 589058
 
7.0%
i 496003
 
5.9%
s 426272
 
5.1%
d 397984
 
4.7%
l 355636
 
4.2%
Other values (49) 2535275
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11418228
> 99.9%
None 1834
 
< 0.1%
Punctuation 159
 
< 0.1%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2121185
18.6%
e 953962
 
8.4%
a 714029
 
6.3%
o 709011
 
6.2%
t 649103
 
5.7%
n 612135
 
5.4%
r 589058
 
5.2%
i 496003
 
4.3%
s 426272
 
3.7%
d 397984
 
3.5%
Other values (86) 3749486
32.8%
None
ValueCountFrequency (%)
â 438
23.9%
€ 411
22.4%
™ 191
10.4%
 127
 
6.9%
à 97
 
5.3%
¦ 96
 
5.2%
¢ 74
 
4.0%
  73
 
4.0%
’ 38
 
2.1%
“ 37
 
2.0%
Other values (27) 252
13.7%
Punctuation
ValueCountFrequency (%)
78
49.1%
19
 
11.9%
18
 
11.3%
18
 
11.3%
9
 
5.7%
8
 
5.0%
6
 
3.8%
3
 
1.9%
Specials
ValueCountFrequency (%)
2
100.0%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
debt_consolidation
18641 
credit_card
5130 
other
3993 
home_improvement
2976 
major_purchase
2187 
Other values (9)
6790 

Length

Max length18
Median length16
Mean length13.736183
Min length3

Characters and Unicode

Total characters545560
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcredit_card
3rd rowcar
4th rowmoving
5th rowother

Common Values

ValueCountFrequency (%)
debt_consolidation 18641
46.9%
credit_card 5130
 
12.9%
other 3993
 
10.1%
home_improvement 2976
 
7.5%
major_purchase 2187
 
5.5%
small_business 1828
 
4.6%
car 1549
 
3.9%
wedding 947
 
2.4%
medical 693
 
1.7%
moving 583
 
1.5%
Other values (4) 1190
 
3.0%

Length

2024-04-10T17:01:41.640863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 18641
46.9%
credit_card 5130
 
12.9%
other 3993
 
10.1%
home_improvement 2976
 
7.5%
major_purchase 2187
 
5.5%
small_business 1828
 
4.6%
car 1549
 
3.9%
wedding 947
 
2.4%
medical 693
 
1.7%
moving 583
 
1.5%
Other values (4) 1190
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o 69725
12.8%
d 50454
9.2%
i 50145
9.2%
t 50087
9.2%
n 44528
8.2%
e 43568
 
8.0%
c 34036
 
6.2%
a 33730
 
6.2%
_ 30865
 
5.7%
s 28521
 
5.2%
Other values (12) 109901
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 514695
94.3%
Connector Punctuation 30865
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 69725
13.5%
d 50454
9.8%
i 50145
9.7%
t 50087
9.7%
n 44528
8.7%
e 43568
8.5%
c 34036
 
6.6%
a 33730
 
6.6%
s 28521
 
5.5%
l 23418
 
4.5%
Other values (11) 86483
16.8%
Connector Punctuation
ValueCountFrequency (%)
_ 30865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 514695
94.3%
Common 30865
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 69725
13.5%
d 50454
9.8%
i 50145
9.7%
t 50087
9.7%
n 44528
8.7%
e 43568
8.5%
c 34036
 
6.6%
a 33730
 
6.6%
s 28521
 
5.5%
l 23418
 
4.5%
Other values (11) 86483
16.8%
Common
ValueCountFrequency (%)
_ 30865
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 545560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 69725
12.8%
d 50454
9.2%
i 50145
9.2%
t 50087
9.2%
n 44528
8.2%
e 43568
 
8.0%
c 34036
 
6.2%
a 33730
 
6.2%
_ 30865
 
5.7%
s 28521
 
5.2%
Other values (12) 109901
20.1%

title
Text

Distinct19615
Distinct (%)49.4%
Missing11
Missing (%)< 0.1%
Memory size310.4 KiB
2024-04-10T17:01:41.908827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length80
Median length72
Mean length17.187327
Min length1

Characters and Unicode

Total characters682440
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17624 ?
Unique (%)44.4%

Sample

1st rowRevolving Debt
2nd rowRejecting new cardmember agreement
3rd rowdjp
4th rowtee_cee
5th rowNewOrganic
ValueCountFrequency (%)
loan 10895
 
10.4%
debt 9245
 
8.8%
consolidation 8622
 
8.2%
credit 4604
 
4.4%
card 3341
 
3.2%
personal 2043
 
2.0%
home 1875
 
1.8%
pay 1344
 
1.3%
off 1259
 
1.2%
my 1133
 
1.1%
Other values (8935) 60203
57.6%
2024-04-10T17:01:42.403983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66029
 
9.7%
o 65729
 
9.6%
n 55657
 
8.2%
e 54557
 
8.0%
a 50167
 
7.4%
i 43822
 
6.4%
t 42600
 
6.2%
d 30679
 
4.5%
r 29153
 
4.3%
s 28544
 
4.2%
Other values (98) 215503
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 521300
76.4%
Uppercase Letter 83242
 
12.2%
Space Separator 66029
 
9.7%
Decimal Number 5995
 
0.9%
Other Punctuation 4442
 
0.7%
Dash Punctuation 824
 
0.1%
Connector Punctuation 213
 
< 0.1%
Close Punctuation 104
 
< 0.1%
Currency Symbol 94
 
< 0.1%
Math Symbol 92
 
< 0.1%
Other values (5) 105
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 65729
12.6%
n 55657
10.7%
e 54557
10.5%
a 50167
9.6%
i 43822
8.4%
t 42600
8.2%
d 30679
 
5.9%
r 29153
 
5.6%
s 28544
 
5.5%
l 26300
 
5.0%
Other values (18) 94092
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 18509
22.2%
L 10335
12.4%
D 9244
11.1%
P 5641
 
6.8%
R 3732
 
4.5%
M 3256
 
3.9%
S 3227
 
3.9%
B 3116
 
3.7%
H 2910
 
3.5%
I 2885
 
3.5%
Other values (18) 20387
24.5%
Other Punctuation
ValueCountFrequency (%)
! 1123
25.3%
' 982
22.1%
. 712
16.0%
/ 538
12.1%
, 435
 
9.8%
& 328
 
7.4%
% 95
 
2.1%
: 64
 
1.4%
" 56
 
1.3%
# 25
 
0.6%
Other values (5) 84
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 1691
28.2%
0 1677
28.0%
2 1105
18.4%
3 299
 
5.0%
5 256
 
4.3%
9 254
 
4.2%
4 216
 
3.6%
6 178
 
3.0%
8 169
 
2.8%
7 150
 
2.5%
Control
ValueCountFrequency (%)
€ 4
21.1%
— 4
21.1%
 4
21.1%
2
10.5%
™ 2
10.5%
– 1
 
5.3%
‚ 1
 
5.3%
… 1
 
5.3%
Math Symbol
ValueCountFrequency (%)
+ 53
57.6%
= 19
 
20.7%
< 9
 
9.8%
> 8
 
8.7%
~ 2
 
2.2%
| 1
 
1.1%
Modifier Symbol
ValueCountFrequency (%)
´ 1
33.3%
` 1
33.3%
^ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 100
96.2%
] 4
 
3.8%
Open Punctuation
ValueCountFrequency (%)
( 77
96.2%
[ 3
 
3.8%
Space Separator
ValueCountFrequency (%)
66029
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 824
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 213
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 94
100.0%
Other Symbol
ValueCountFrequency (%)
¦ 2
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 604542
88.6%
Common 77898
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 65729
 
10.9%
n 55657
 
9.2%
e 54557
 
9.0%
a 50167
 
8.3%
i 43822
 
7.2%
t 42600
 
7.0%
d 30679
 
5.1%
r 29153
 
4.8%
s 28544
 
4.7%
l 26300
 
4.4%
Other values (46) 177334
29.3%
Common
ValueCountFrequency (%)
66029
84.8%
1 1691
 
2.2%
0 1677
 
2.2%
! 1123
 
1.4%
2 1105
 
1.4%
' 982
 
1.3%
- 824
 
1.1%
. 712
 
0.9%
/ 538
 
0.7%
, 435
 
0.6%
Other values (42) 2782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 682408
> 99.9%
None 32
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66029
 
9.7%
o 65729
 
9.6%
n 55657
 
8.2%
e 54557
 
8.0%
a 50167
 
7.4%
i 43822
 
6.4%
t 42600
 
6.2%
d 30679
 
4.5%
r 29153
 
4.3%
s 28544
 
4.2%
Other values (84) 215471
31.6%
None
ValueCountFrequency (%)
€ 4
12.5%
— 4
12.5%
 4
12.5%
î 4
12.5%
â 4
12.5%
à 2
6.2%
¦ 2
6.2%
™ 2
6.2%
– 1
 
3.1%
´ 1
 
3.1%
Other values (4) 4
12.5%
Distinct823
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:42.728005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters198585
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.1%

Sample

1st row146xx
2nd row775xx
3rd row481xx
4th row088xx
5th row441xx
ValueCountFrequency (%)
100xx 597
 
1.5%
945xx 545
 
1.4%
112xx 516
 
1.3%
606xx 503
 
1.3%
070xx 473
 
1.2%
900xx 453
 
1.1%
021xx 397
 
1.0%
300xx 394
 
1.0%
926xx 371
 
0.9%
750xx 367
 
0.9%
Other values (813) 35101
88.4%
2024-04-10T17:01:43.191974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 79434
40.0%
0 19773
 
10.0%
1 15629
 
7.9%
2 13589
 
6.8%
9 12681
 
6.4%
3 12356
 
6.2%
7 10257
 
5.2%
4 9121
 
4.6%
5 9020
 
4.5%
8 8670
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119151
60.0%
Lowercase Letter 79434
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19773
16.6%
1 15629
13.1%
2 13589
11.4%
9 12681
10.6%
3 12356
10.4%
7 10257
8.6%
4 9121
7.7%
5 9020
7.6%
8 8670
7.3%
6 8055
6.8%
Lowercase Letter
ValueCountFrequency (%)
x 79434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119151
60.0%
Latin 79434
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19773
16.6%
1 15629
13.1%
2 13589
11.4%
9 12681
10.6%
3 12356
10.4%
7 10257
8.6%
4 9121
7.7%
5 9020
7.6%
8 8670
7.3%
6 8055
6.8%
Latin
ValueCountFrequency (%)
x 79434
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 79434
40.0%
0 19773
 
10.0%
1 15629
 
7.9%
2 13589
 
6.8%
9 12681
 
6.4%
3 12356
 
6.2%
7 10257
 
5.2%
4 9121
 
4.6%
5 9020
 
4.5%
8 8670
 
4.4%

addr_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
CA
7099 
NY
3812 
FL
2866 
TX
2727 
NJ
 
1850
Other values (45)
21363 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters79434
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowTX
3rd rowMI
4th rowNJ
5th rowOH

Common Values

ValueCountFrequency (%)
CA 7099
17.9%
NY 3812
 
9.6%
FL 2866
 
7.2%
TX 2727
 
6.9%
NJ 1850
 
4.7%
IL 1525
 
3.8%
PA 1517
 
3.8%
VA 1407
 
3.5%
GA 1398
 
3.5%
MA 1340
 
3.4%
Other values (40) 14176
35.7%

Length

2024-04-10T17:01:43.373578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 7099
17.9%
ny 3812
 
9.6%
fl 2866
 
7.2%
tx 2727
 
6.9%
nj 1850
 
4.7%
il 1525
 
3.8%
pa 1517
 
3.8%
va 1407
 
3.5%
ga 1398
 
3.5%
ma 1340
 
3.4%
Other values (40) 14176
35.7%

Most occurring characters

ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79434
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 79434
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15698
19.8%
C 10116
12.7%
N 7953
10.0%
L 5279
 
6.6%
M 4706
 
5.9%
Y 4220
 
5.3%
T 3892
 
4.9%
O 3451
 
4.3%
I 3097
 
3.9%
F 2866
 
3.6%
Other values (14) 18156
22.9%

dti
Real number (ℝ)

Distinct2868
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.31513
Minimum0
Maximum29.99
Zeros183
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:43.514713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.13
Q18.17
median13.4
Q318.6
95-th percentile23.84
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.6785936
Coefficient of variation (CV)0.50157932
Kurtosis-0.85201548
Mean13.31513
Median Absolute Deviation (MAD)5.21
Skewness-0.028043331
Sum528837
Variance44.603612
MonotonicityNot monotonic
2024-04-10T17:01:43.693876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183
 
0.5%
12 51
 
0.1%
18 45
 
0.1%
19.2 40
 
0.1%
13.2 39
 
0.1%
16.8 38
 
0.1%
12.48 38
 
0.1%
13.5 38
 
0.1%
6 37
 
0.1%
14.29 36
 
0.1%
Other values (2858) 39172
98.6%
ValueCountFrequency (%)
0 183
0.5%
0.01 3
 
< 0.1%
0.02 5
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
 
< 0.1%
0.08 5
 
< 0.1%
0.09 3
 
< 0.1%
ValueCountFrequency (%)
29.99 1
 
< 0.1%
29.95 1
 
< 0.1%
29.93 3
< 0.1%
29.92 2
< 0.1%
29.89 1
 
< 0.1%
29.88 1
 
< 0.1%
29.86 2
< 0.1%
29.85 1
 
< 0.1%
29.83 1
 
< 0.1%
29.82 1
 
< 0.1%

delinq_2yrs
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14651157
Minimum0
Maximum11
Zeros35405
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:43.841016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49181152
Coefficient of variation (CV)3.3568101
Kurtosis39.4125
Mean0.14651157
Median Absolute Deviation (MAD)0
Skewness5.0220352
Sum5819
Variance0.24187857
MonotonicityNot monotonic
2024-04-10T17:01:43.972533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 35405
89.1%
1 3303
 
8.3%
2 687
 
1.7%
3 220
 
0.6%
4 62
 
0.2%
5 22
 
0.1%
6 10
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 35405
89.1%
1 3303
 
8.3%
2 687
 
1.7%
3 220
 
0.6%
4 62
 
0.2%
5 22
 
0.1%
6 10
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 10
 
< 0.1%
5 22
 
0.1%
4 62
 
0.2%
3 220
 
0.6%
2 687
 
1.7%
1 3303
8.3%
Distinct526
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
Minimum1946-01-01 00:00:00
Maximum2008-01-11 00:00:00
2024-04-10T17:01:44.143068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:44.326870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

inq_last_6mths
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86919959
Minimum0
Maximum8
Zeros19300
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:44.496101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0702193
Coefficient of variation (CV)1.23127
Kurtosis2.5621599
Mean0.86919959
Median Absolute Deviation (MAD)1
Skewness1.3903909
Sum34522
Variance1.1453694
MonotonicityNot monotonic
2024-04-10T17:01:44.628464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 19300
48.6%
1 10971
27.6%
2 5812
 
14.6%
3 3048
 
7.7%
4 326
 
0.8%
5 146
 
0.4%
6 64
 
0.2%
7 35
 
0.1%
8 15
 
< 0.1%
ValueCountFrequency (%)
0 19300
48.6%
1 10971
27.6%
2 5812
 
14.6%
3 3048
 
7.7%
4 326
 
0.8%
5 146
 
0.4%
6 64
 
0.2%
7 35
 
0.1%
8 15
 
< 0.1%
ValueCountFrequency (%)
8 15
 
< 0.1%
7 35
 
0.1%
6 64
 
0.2%
5 146
 
0.4%
4 326
 
0.8%
3 3048
 
7.7%
2 5812
 
14.6%
1 10971
27.6%
0 19300
48.6%

mths_since_last_delinq
Real number (ℝ)

MISSING  ZEROS 

Distinct95
Distinct (%)0.7%
Missing25682
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean35.900962
Minimum0
Maximum120
Zeros443
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:44.796660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median34
Q352
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.02006
Coefficient of variation (CV)0.6133557
Kurtosis-0.84257778
Mean35.900962
Median Absolute Deviation (MAD)17
Skewness0.30643687
Sum503870
Variance484.88302
MonotonicityNot monotonic
2024-04-10T17:01:45.139819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 443
 
1.1%
15 252
 
0.6%
23 247
 
0.6%
30 247
 
0.6%
24 241
 
0.6%
19 238
 
0.6%
38 237
 
0.6%
20 233
 
0.6%
18 231
 
0.6%
22 231
 
0.6%
Other values (85) 11435
28.8%
(Missing) 25682
64.7%
ValueCountFrequency (%)
0 443
1.1%
1 30
 
0.1%
2 101
 
0.3%
3 145
 
0.4%
4 153
 
0.4%
5 151
 
0.4%
6 192
0.5%
7 176
 
0.4%
8 168
 
0.4%
9 182
0.5%
ValueCountFrequency (%)
120 1
< 0.1%
115 1
< 0.1%
107 1
< 0.1%
106 1
< 0.1%
103 2
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
89 1
< 0.1%
86 2
< 0.1%

mths_since_last_record
Real number (ℝ)

MISSING  ZEROS 

Distinct111
Distinct (%)4.0%
Missing36931
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean69.698134
Minimum0
Maximum129
Zeros670
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:45.331429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122
median90
Q3104
95-th percentile115
Maximum129
Range129
Interquartile range (IQR)82

Descriptive statistics

Standard deviation43.822529
Coefficient of variation (CV)0.62874753
Kurtosis-1.1565557
Mean69.698134
Median Absolute Deviation (MAD)20
Skewness-0.71722858
Sum194179
Variance1920.4141
MonotonicityNot monotonic
2024-04-10T17:01:45.523021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 670
 
1.7%
104 61
 
0.2%
89 60
 
0.2%
113 59
 
0.1%
111 57
 
0.1%
94 55
 
0.1%
108 55
 
0.1%
87 54
 
0.1%
93 54
 
0.1%
88 53
 
0.1%
Other values (101) 1608
 
4.0%
(Missing) 36931
93.0%
ValueCountFrequency (%)
0 670
1.7%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
17 3
 
< 0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
120 1
 
< 0.1%
119 10
 
< 0.1%
118 36
0.1%
117 47
0.1%
116 41
0.1%
115 37
0.1%
114 51
0.1%
113 59
0.1%
112 39
0.1%

open_acc
Real number (ℝ)

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2944079
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:45.684178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4002825
Coefficient of variation (CV)0.47343333
Kurtosis1.677572
Mean9.2944079
Median Absolute Deviation (MAD)3
Skewness1.0037619
Sum369146
Variance19.362486
MonotonicityNot monotonic
2024-04-10T17:01:45.854467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7 4018
10.1%
6 3946
9.9%
8 3936
9.9%
9 3718
9.4%
10 3223
 
8.1%
5 3183
 
8.0%
11 2746
 
6.9%
4 2343
 
5.9%
12 2273
 
5.7%
13 1911
 
4.8%
Other values (30) 8420
21.2%
ValueCountFrequency (%)
2 605
 
1.5%
3 1493
 
3.8%
4 2343
5.9%
5 3183
8.0%
6 3946
9.9%
7 4018
10.1%
8 3936
9.9%
9 3718
9.4%
10 3223
8.1%
11 2746
6.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 2
 
< 0.1%
35 4
< 0.1%
34 5
< 0.1%
33 3
< 0.1%
32 4
< 0.1%

pub_rec
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
0
37601 
1
 
2056
2
 
51
3
 
7
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39717
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Length

2024-04-10T17:01:45.985983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:46.124040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39717
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 39717
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39717
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37601
94.7%
1 2056
 
5.2%
2 51
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

revol_bal
Real number (ℝ)

ZEROS 

Distinct21711
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13382.528
Minimum0
Maximum149588
Zeros994
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:46.274226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile321.8
Q13703
median8850
Q317058
95-th percentile41656.4
Maximum149588
Range149588
Interquartile range (IQR)13355

Descriptive statistics

Standard deviation15885.017
Coefficient of variation (CV)1.1869967
Kurtosis14.896523
Mean13382.528
Median Absolute Deviation (MAD)6027
Skewness3.1908837
Sum5.3151387 × 108
Variance2.5233375 × 108
MonotonicityNot monotonic
2024-04-10T17:01:46.456128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 994
 
2.5%
255 14
 
< 0.1%
298 14
 
< 0.1%
1 12
 
< 0.1%
682 11
 
< 0.1%
1763 9
 
< 0.1%
10 9
 
< 0.1%
39 9
 
< 0.1%
6 9
 
< 0.1%
1159 9
 
< 0.1%
Other values (21701) 38627
97.3%
ValueCountFrequency (%)
0 994
2.5%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 6
 
< 0.1%
4 3
 
< 0.1%
5 8
 
< 0.1%
6 9
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
149588 1
< 0.1%
149527 1
< 0.1%
149000 1
< 0.1%
148829 1
< 0.1%
148804 1
< 0.1%
147897 1
< 0.1%
147750 1
< 0.1%
147559 1
< 0.1%
147451 1
< 0.1%
147365 1
< 0.1%
Distinct1089
Distinct (%)2.7%
Missing50
Missing (%)0.1%
Memory size310.4 KiB
2024-04-10T17:01:46.778871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8869085
Min length5

Characters and Unicode

Total characters233516
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.2%

Sample

1st row52.70%
2nd row39.50%
3rd row68.60%
4th row88.40%
5th row23.20%
ValueCountFrequency (%)
0.00 977
 
2.5%
0.20 63
 
0.2%
63.00 62
 
0.2%
0.10 58
 
0.1%
66.70 58
 
0.1%
40.70 58
 
0.1%
31.20 57
 
0.1%
61.00 57
 
0.1%
66.60 57
 
0.1%
46.40 57
 
0.1%
Other values (1079) 38163
96.2%
2024-04-10T17:01:47.263144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 49323
21.1%
. 39667
17.0%
% 39667
17.0%
4 12082
 
5.2%
5 12063
 
5.2%
6 11989
 
5.1%
7 11949
 
5.1%
3 11885
 
5.1%
2 11550
 
4.9%
8 11419
 
4.9%
Other values (2) 21922
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 154182
66.0%
Other Punctuation 79334
34.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49323
32.0%
4 12082
 
7.8%
5 12063
 
7.8%
6 11989
 
7.8%
7 11949
 
7.7%
3 11885
 
7.7%
2 11550
 
7.5%
8 11419
 
7.4%
1 11111
 
7.2%
9 10811
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 39667
50.0%
% 39667
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 233516
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49323
21.1%
. 39667
17.0%
% 39667
17.0%
4 12082
 
5.2%
5 12063
 
5.2%
6 11989
 
5.1%
7 11949
 
5.1%
3 11885
 
5.1%
2 11550
 
4.9%
8 11419
 
4.9%
Other values (2) 21922
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49323
21.1%
. 39667
17.0%
% 39667
17.0%
4 12082
 
5.2%
5 12063
 
5.2%
6 11989
 
5.1%
7 11949
 
5.1%
3 11885
 
5.1%
2 11550
 
4.9%
8 11419
 
4.9%
Other values (2) 21922
9.4%

total_acc
Real number (ℝ)

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.088828
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:47.453227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.401709
Coefficient of variation (CV)0.51617534
Kurtosis0.6937402
Mean22.088828
Median Absolute Deviation (MAD)7
Skewness0.82737909
Sum877302
Variance129.99896
MonotonicityNot monotonic
2024-04-10T17:01:47.632139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1471
 
3.7%
15 1462
 
3.7%
17 1457
 
3.7%
14 1445
 
3.6%
20 1428
 
3.6%
18 1422
 
3.6%
21 1412
 
3.6%
13 1385
 
3.5%
19 1341
 
3.4%
12 1325
 
3.3%
Other values (72) 25569
64.4%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 182
 
0.5%
4 420
 
1.1%
5 552
1.4%
6 683
1.7%
7 828
2.1%
8 1006
2.5%
9 1080
2.7%
10 1193
3.0%
11 1278
3.2%
ValueCountFrequency (%)
90 1
< 0.1%
87 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 2
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 2
< 0.1%
75 2
< 0.1%
74 1
< 0.1%

initial_list_status
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
False
39717 
ValueCountFrequency (%)
False 39717
100.0%
2024-04-10T17:01:47.769595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

out_prncp
Real number (ℝ)

ZEROS 

Distinct1137
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.227887
Minimum0
Maximum6311.47
Zeros38577
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:47.900769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6311.47
Range6311.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation375.17284
Coefficient of variation (CV)7.3236055
Kurtosis97.658555
Mean51.227887
Median Absolute Deviation (MAD)0
Skewness9.22673
Sum2034618
Variance140754.66
MonotonicityNot monotonic
2024-04-10T17:01:48.070139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38577
97.1%
2963.24 2
 
< 0.1%
827.13 2
 
< 0.1%
2277.11 2
 
< 0.1%
1972.6 2
 
< 0.1%
1347.43 1
 
< 0.1%
2540.31 1
 
< 0.1%
1978.94 1
 
< 0.1%
1231.2 1
 
< 0.1%
2614.43 1
 
< 0.1%
Other values (1127) 1127
 
2.8%
ValueCountFrequency (%)
0 38577
97.1%
10.26 1
 
< 0.1%
11.91 1
 
< 0.1%
13.28 1
 
< 0.1%
19.12 1
 
< 0.1%
27.41 1
 
< 0.1%
40.65 1
 
< 0.1%
50.46 1
 
< 0.1%
53 1
 
< 0.1%
57.67 1
 
< 0.1%
ValueCountFrequency (%)
6311.47 1
< 0.1%
6308.37 1
< 0.1%
6307.37 1
< 0.1%
6307.15 1
< 0.1%
6219.16 1
< 0.1%
6219.11 1
< 0.1%
6182.86 1
< 0.1%
6071.68 1
< 0.1%
6034.37 1
< 0.1%
6027.7 1
< 0.1%

out_prncp_inv
Real number (ℝ)

ZEROS 

Distinct1138
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.989768
Minimum0
Maximum6307.37
Zeros38577
Zeros (%)97.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:48.254963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6307.37
Range6307.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation373.82446
Coefficient of variation (CV)7.3313622
Kurtosis98.040553
Mean50.989768
Median Absolute Deviation (MAD)0
Skewness9.2437655
Sum2025160.6
Variance139744.72
MonotonicityNot monotonic
2024-04-10T17:01:48.422024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38577
97.1%
827.13 2
 
< 0.1%
1972.6 2
 
< 0.1%
1664.64 2
 
< 0.1%
1228.61 1
 
< 0.1%
2614.43 1
 
< 0.1%
1323.6 1
 
< 0.1%
1049.49 1
 
< 0.1%
1232.19 1
 
< 0.1%
1243.6 1
 
< 0.1%
Other values (1128) 1128
 
2.8%
ValueCountFrequency (%)
0 38577
97.1%
10.26 1
 
< 0.1%
11.91 1
 
< 0.1%
13.28 1
 
< 0.1%
19.09 1
 
< 0.1%
27.41 1
 
< 0.1%
40.65 1
 
< 0.1%
50.46 1
 
< 0.1%
53 1
 
< 0.1%
57.67 1
 
< 0.1%
ValueCountFrequency (%)
6307.37 1
< 0.1%
6306.96 1
< 0.1%
6298.11 1
< 0.1%
6276.75 1
< 0.1%
6219.16 1
< 0.1%
6183.55 1
< 0.1%
6182.86 1
< 0.1%
6067.33 1
< 0.1%
6034.37 1
< 0.1%
6027.7 1
< 0.1%

total_pymnt
Real number (ℝ)

Distinct36591
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12153.597
Minimum0
Maximum58563.68
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:48.601881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1887.954
Q15576.93
median9899.64
Q316534.43
95-th percentile30245.116
Maximum58563.68
Range58563.68
Interquartile range (IQR)10957.5

Descriptive statistics

Standard deviation9042.0408
Coefficient of variation (CV)0.74398066
Kurtosis1.9858943
Mean12153.597
Median Absolute Deviation (MAD)5016.76
Skewness1.3398574
Sum4.8270439 × 108
Variance81758501
MonotonicityNot monotonic
2024-04-10T17:01:48.771184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11196.57 26
 
0.1%
6514.52 19
 
< 0.1%
10956.78 17
 
< 0.1%
13148.14 17
 
< 0.1%
0 16
 
< 0.1%
6717.95 16
 
< 0.1%
11784.23 16
 
< 0.1%
5478.39 15
 
< 0.1%
11907.35 14
 
< 0.1%
13517.36 13
 
< 0.1%
Other values (36581) 39548
99.6%
ValueCountFrequency (%)
0 16
< 0.1%
33.73 1
 
< 0.1%
35.71 1
 
< 0.1%
44.92 2
 
< 0.1%
44.96 1
 
< 0.1%
61.71 1
 
< 0.1%
62.86 1
 
< 0.1%
66.77 1
 
< 0.1%
67.32 1
 
< 0.1%
69.64 1
 
< 0.1%
ValueCountFrequency (%)
58563.68 1
< 0.1%
58480.14 1
< 0.1%
57835.28 1
< 0.1%
56849.27 1
< 0.1%
56662.59 1
< 0.1%
56199.44 1
< 0.1%
55906.95 1
< 0.1%
55768.78 1
< 0.1%
55368.41 1
< 0.1%
55139 1
< 0.1%

total_pymnt_inv
Real number (ℝ)

Distinct37518
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11567.149
Minimum0
Maximum58563.68
Zeros165
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:48.955851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1420.408
Q15112.31
median9287.15
Q315798.81
95-th percentile29627.236
Maximum58563.68
Range58563.68
Interquartile range (IQR)10686.5

Descriptive statistics

Standard deviation8942.6726
Coefficient of variation (CV)0.77310948
Kurtosis2.0297585
Mean11567.149
Median Absolute Deviation (MAD)4939.58
Skewness1.3548376
Sum4.5941246 × 108
Variance79971393
MonotonicityNot monotonic
2024-04-10T17:01:49.134239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165
 
0.4%
6514.52 16
 
< 0.1%
5478.39 14
 
< 0.1%
13148.14 14
 
< 0.1%
10956.78 12
 
< 0.1%
6717.95 12
 
< 0.1%
11196.57 12
 
< 0.1%
7328.92 11
 
< 0.1%
13517.36 11
 
< 0.1%
5557.03 11
 
< 0.1%
Other values (37508) 39439
99.3%
ValueCountFrequency (%)
0 165
0.4%
0.54 1
 
< 0.1%
12.65 1
 
< 0.1%
18.97 1
 
< 0.1%
21.6 1
 
< 0.1%
25.18 1
 
< 0.1%
26.19 1
 
< 0.1%
33.73 1
 
< 0.1%
33.99 1
 
< 0.1%
35.71 1
 
< 0.1%
ValueCountFrequency (%)
58563.68 1
< 0.1%
58438.37 1
< 0.1%
57628.73 1
< 0.1%
56622.12 1
< 0.1%
56515.16 1
< 0.1%
55867.02 1
< 0.1%
55579.28 1
< 0.1%
55066.92 1
< 0.1%
54675.68 1
< 0.1%
54315.94 1
< 0.1%

total_rec_prncp
Real number (ℝ)

Distinct7976
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9793.3488
Minimum0
Maximum35000.02
Zeros74
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:49.303437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1339.842
Q14600
median8000
Q313653.26
95-th percentile24999.982
Maximum35000.02
Range35000.02
Interquartile range (IQR)9053.26

Descriptive statistics

Standard deviation7065.5221
Coefficient of variation (CV)0.7214613
Kurtosis1.1033555
Mean9793.3488
Median Absolute Deviation (MAD)4000
Skewness1.1182545
Sum3.8896243 × 108
Variance49921603
MonotonicityNot monotonic
2024-04-10T17:01:49.488166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2293
 
5.8%
12000 1805
 
4.5%
5000 1702
 
4.3%
6000 1637
 
4.1%
15000 1400
 
3.5%
8000 1318
 
3.3%
20000 1059
 
2.7%
4000 956
 
2.4%
3000 883
 
2.2%
7000 851
 
2.1%
Other values (7966) 25813
65.0%
ValueCountFrequency (%)
0 74
0.2%
21.21 1
 
< 0.1%
21.93 1
 
< 0.1%
22.24 1
 
< 0.1%
22.5 1
 
< 0.1%
24.87 1
 
< 0.1%
30.32 1
 
< 0.1%
32.51 1
 
< 0.1%
34.5 1
 
< 0.1%
35.14 1
 
< 0.1%
ValueCountFrequency (%)
35000.02 2
 
< 0.1%
35000.01 1
 
< 0.1%
35000 363
0.9%
34999.99 5
 
< 0.1%
34999.98 1
 
< 0.1%
34999.97 1
 
< 0.1%
34911.47 1
 
< 0.1%
34800 1
 
< 0.1%
34793.43 1
 
< 0.1%
34675 1
 
< 0.1%

total_rec_int
Real number (ℝ)

Distinct35148
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2263.6632
Minimum0
Maximum23563.68
Zeros71
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:49.657419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.168
Q1662.18
median1348.91
Q32833.4
95-th percentile7575.812
Maximum23563.68
Range23563.68
Interquartile range (IQR)2171.22

Descriptive statistics

Standard deviation2608.112
Coefficient of variation (CV)1.1521643
Kurtosis9.6882784
Mean2263.6632
Median Absolute Deviation (MAD)866.01
Skewness2.6687472
Sum89905910
Variance6802248
MonotonicityNot monotonic
2024-04-10T17:01:49.826449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
0.2%
1196.57 26
 
0.1%
514.52 19
 
< 0.1%
1784.23 17
 
< 0.1%
717.95 17
 
< 0.1%
1148.14 17
 
< 0.1%
956.78 17
 
< 0.1%
478.39 16
 
< 0.1%
1907.35 14
 
< 0.1%
1435.9 13
 
< 0.1%
Other values (35138) 39490
99.4%
ValueCountFrequency (%)
0 71
0.2%
6.22 1
 
< 0.1%
6.27 1
 
< 0.1%
7.19 1
 
< 0.1%
7.2 2
 
< 0.1%
8.23 1
 
< 0.1%
9.34 1
 
< 0.1%
9.49 1
 
< 0.1%
9.58 2
 
< 0.1%
10.26 1
 
< 0.1%
ValueCountFrequency (%)
23563.68 1
< 0.1%
23506.56 1
< 0.1%
23480.14 1
< 0.1%
22835.28 1
< 0.1%
22716.42 1
< 0.1%
22594.16 1
< 0.1%
22593.34 1
< 0.1%
22593.04 1
< 0.1%
22587.51 1
< 0.1%
22422.33 1
< 0.1%

total_rec_late_fee
Real number (ℝ)

ZEROS 

Distinct801
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3630194
Minimum0
Maximum180.2
Zeros37671
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:49.990697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.92
Maximum180.2
Range180.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.2899931
Coefficient of variation (CV)5.3484149
Kurtosis100.85133
Mean1.3630194
Median Absolute Deviation (MAD)0
Skewness8.4295237
Sum54135.04
Variance53.143999
MonotonicityNot monotonic
2024-04-10T17:01:50.173186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37671
94.8%
15 604
 
1.5%
30 123
 
0.3%
14.98 68
 
0.2%
14.99 53
 
0.1%
14.97 42
 
0.1%
45 38
 
0.1%
14.96 33
 
0.1%
14.94 27
 
0.1%
14.95 24
 
0.1%
Other values (791) 1034
 
2.6%
ValueCountFrequency (%)
0 37671
94.8%
0.01 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.1 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 2
 
< 0.1%
0.27 1
 
< 0.1%
0.3 1
 
< 0.1%
0.65 1
 
< 0.1%
ValueCountFrequency (%)
180.2 1
< 0.1%
166.43 1
< 0.1%
165.69 1
< 0.1%
146.6 1
< 0.1%
146.04 1
< 0.1%
134.07 1
< 0.1%
130.6 1
< 0.1%
130.47 1
< 0.1%
127.79 1
< 0.1%
121.93 1
< 0.1%

recoveries
Real number (ℝ)

ZEROS 

Distinct4040
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.221624
Minimum0
Maximum29623.35
Zeros35499
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:50.358109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile362.418
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation688.74477
Coefficient of variation (CV)7.233071
Kurtosis379.37757
Mean95.221624
Median Absolute Deviation (MAD)0
Skewness16.519378
Sum3781917.2
Variance474369.36
MonotonicityNot monotonic
2024-04-10T17:01:50.523040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35499
89.4%
10.4 4
 
< 0.1%
11.29 4
 
< 0.1%
11.2 3
 
< 0.1%
12.09 3
 
< 0.1%
44.92 3
 
< 0.1%
10.13 3
 
< 0.1%
14.61 3
 
< 0.1%
13 3
 
< 0.1%
13.93 3
 
< 0.1%
Other values (4030) 4189
 
10.5%
ValueCountFrequency (%)
0 35499
89.4%
6.3 1
 
< 0.1%
6.31 1
 
< 0.1%
8.19 1
 
< 0.1%
8.36 1
 
< 0.1%
8.41 1
 
< 0.1%
8.46 1
 
< 0.1%
8.56 1
 
< 0.1%
8.71 1
 
< 0.1%
8.88 1
 
< 0.1%
ValueCountFrequency (%)
29623.35 1
< 0.1%
22943.37 1
< 0.1%
21810.31 1
< 0.1%
20006.53 1
< 0.1%
19915.67 1
< 0.1%
19508.26 1
< 0.1%
18694.32 1
< 0.1%
16560.06 1
< 0.1%
16502.69 1
< 0.1%
16268.35 1
< 0.1%

collection_recovery_fee
Real number (ℝ)

SKEWED  ZEROS 

Distinct2118
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.406114
Minimum0
Maximum7002.19
Zeros35935
Zeros (%)90.5%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:50.689807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.152
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation148.6716
Coefficient of variation (CV)11.983736
Kurtosis821.30052
Mean12.406114
Median Absolute Deviation (MAD)0
Skewness25.029416
Sum492733.63
Variance22103.245
MonotonicityNot monotonic
2024-04-10T17:01:51.025079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35935
90.5%
2 12
 
< 0.1%
1.2 12
 
< 0.1%
0.8 11
 
< 0.1%
1.69 10
 
< 0.1%
3.23 10
 
< 0.1%
2.08 10
 
< 0.1%
3.71 9
 
< 0.1%
3.2 9
 
< 0.1%
1.6 9
 
< 0.1%
Other values (2108) 3690
 
9.3%
ValueCountFrequency (%)
0 35935
90.5%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2 3
 
< 0.1%
0.21 1
 
< 0.1%
0.22 1
 
< 0.1%
0.23 2
 
< 0.1%
ValueCountFrequency (%)
7002.19 1
< 0.1%
6972.59 1
< 0.1%
6543.04 1
< 0.1%
5774.8 1
< 0.1%
5602.72 1
< 0.1%
5569.92 1
< 0.1%
5216.74 1
< 0.1%
5036.01 1
< 0.1%
4902.08 1
< 0.1%
4900.75 1
< 0.1%
Distinct101
Distinct (%)0.3%
Missing71
Missing (%)0.2%
Memory size310.4 KiB
Minimum2008-01-01 00:00:00
Maximum2016-01-05 00:00:00
2024-04-10T17:01:51.216986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:51.401554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

last_pymnt_amnt
Real number (ℝ)

Distinct34930
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2678.8262
Minimum0
Maximum36115.2
Zeros74
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size310.4 KiB
2024-04-10T17:01:51.573992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.34
Q1218.68
median546.14
Q33293.16
95-th percentile12183.944
Maximum36115.2
Range36115.2
Interquartile range (IQR)3074.48

Descriptive statistics

Standard deviation4447.136
Coefficient of variation (CV)1.6601062
Kurtosis8.8678197
Mean2678.8262
Median Absolute Deviation (MAD)449.45
Skewness2.7121222
Sum1.0639494 × 108
Variance19777019
MonotonicityNot monotonic
2024-04-10T17:01:51.748895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
0.2%
276.06 21
 
0.1%
200 17
 
< 0.1%
50 16
 
< 0.1%
100 15
 
< 0.1%
400 12
 
< 0.1%
773.44 12
 
< 0.1%
786.01 11
 
< 0.1%
500 11
 
< 0.1%
150 11
 
< 0.1%
Other values (34920) 39517
99.5%
ValueCountFrequency (%)
0 74
0.2%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.28 1
 
< 0.1%
ValueCountFrequency (%)
36115.2 1
< 0.1%
35613.68 1
< 0.1%
35596.41 1
< 0.1%
35479.89 1
< 0.1%
35471.86 1
< 0.1%
35395.59 1
< 0.1%
35339.05 1
< 0.1%
35337.09 1
< 0.1%
35322.96 1
< 0.1%
35322.6 1
< 0.1%

next_pymnt_d
Date

MISSING 

Distinct2
Distinct (%)0.2%
Missing38577
Missing (%)97.1%
Memory size310.4 KiB
Minimum2016-01-06 00:00:00
Maximum2016-01-07 00:00:00
2024-04-10T17:01:51.902493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:52.018357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
Distinct106
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size310.4 KiB
Minimum2007-01-05 00:00:00
Maximum2016-01-05 00:00:00
2024-04-10T17:01:52.165235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:52.334509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

application_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size310.4 KiB
INDIVIDUAL
39717 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters397170
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 39717
100.0%

Length

2024-04-10T17:01:52.503808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:52.609353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
individual 39717
100.0%

Most occurring characters

ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 397170
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397170
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 397170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 119151
30.0%
D 79434
20.0%
N 39717
 
10.0%
V 39717
 
10.0%
U 39717
 
10.0%
A 39717
 
10.0%
L 39717
 
10.0%

pub_rec_bankruptcies
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing697
Missing (%)1.8%
Memory size310.4 KiB
0.0
37339 
1.0
 
1674
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters117060
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 37339
94.0%
1.0 1674
 
4.2%
2.0 7
 
< 0.1%
(Missing) 697
 
1.8%

Length

2024-04-10T17:01:52.734936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T17:01:52.866455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 37339
95.7%
1.0 1674
 
4.3%
2.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 76359
65.2%
. 39020
33.3%
1 1674
 
1.4%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78040
66.7%
Other Punctuation 39020
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76359
97.8%
1 1674
 
2.1%
2 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 39020
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76359
65.2%
. 39020
33.3%
1 1674
 
1.4%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76359
65.2%
. 39020
33.3%
1 1674
 
1.4%
2 7
 
< 0.1%

Interactions

2024-04-10T17:01:27.727679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T16:59:58.966409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.920549image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:06.319638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:10.138895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:14.008209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:17.661430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:21.029112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:24.636649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:28.079188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:31.651546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:35.465128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:40.361238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.950621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:47.450538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.985048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:54.482631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:58.590680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:02.445794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:06.188769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:09.683693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:13.418688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:16.861581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:20.702864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:24.249346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:27.913600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T16:59:59.192693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:03.056669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:06.431429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:10.315614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:14.131044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:17.785799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:21.170773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:24.766629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:28.213750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:31.793368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:35.595170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:40.478274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:44.068887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:47.582704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:51.096008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:54.669000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-04-10T17:01:00.956596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.066851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:08.561369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:12.110224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:15.594962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:19.505806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.146026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:26.613394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:30.461247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:01.995286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:05.331509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:08.897953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:12.980039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:16.530969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.067029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:23.665715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.162166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:30.630744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:34.285204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:39.173033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:42.978328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:46.439277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:49.985638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:53.312583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:57.502066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:01.099009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.200287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:08.711346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:12.428288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:15.787948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:19.712534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.277892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:26.753858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:30.595367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.114923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:05.484179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:09.190344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:13.133652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:16.681324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.200662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:23.818724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.280439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:30.765502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:34.424281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:39.339676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.107530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:46.570868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.111341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:53.446992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:57.681875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:01.234747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.337974image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:08.846154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:12.579658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:15.942135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:19.867977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.432870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:26.899749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:30.735698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.258820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:05.620627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:09.313184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:13.307521image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:16.812322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.343813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:23.957257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.433671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:30.915717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:34.584264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:39.504336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.285116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:46.701060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.253635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:53.747905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:57.834933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:01.375088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.476837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:08.998062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:12.717397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:16.116571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:20.012481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.570076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:27.045735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:30.864104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.384185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:05.748907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:09.453156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:13.431055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:16.951618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.482208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:24.085823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.553913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:31.048872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:34.878760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:39.669699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.404810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:46.811313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.404074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:53.868092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:57.951711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:01.499645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.618993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:09.111873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:12.848686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:16.282247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:20.133825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.694854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:27.176685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:30.992655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.514817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:05.910449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:09.641845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:13.586914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:17.079259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.620333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:24.212263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.678882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:31.221281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:35.027072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:39.814275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.547677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:46.948801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.537903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:54.001978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:58.116550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:01.640220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.750260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:09.264967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:12.976703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:16.412166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:20.276253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.833786image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:27.320047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:31.146543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.631599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:06.031436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:09.806747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:13.725266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:17.229642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.744034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:24.354724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.818653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:31.371683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:35.163952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:40.053111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.680839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:47.079548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.694437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:54.130543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:58.299536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:02.009329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:05.900097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:09.400006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:13.131326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:16.601641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:20.415272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:23.970866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:27.454997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:31.296945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:02.788656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:06.184438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:09.989959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:13.871668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:17.513374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:20.903755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:24.497617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:27.955444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:31.511907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:35.315046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:40.216032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:43.795276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:47.281964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:50.838376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:54.316201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:00:58.449380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:02.216799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:06.047695image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:09.543487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:13.260666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:16.735051image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:20.542604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:24.112311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-10T17:01:27.599879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-10T17:01:31.622781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-10T17:01:32.149597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_dapplication_typepub_rec_bankruptcies
06900126553315000.015000.014875.0036 months8.94%476.58AA5NaN< 1 yearMORTGAGE110000.0Not Verified01-09-2009Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=69001Taking advantage of excellent credit to pay off credit cardcredit_cardRevolving Debt146xxNY7.070.001-11-199110.00.0607586.052.70%19f0.00.017135.5116992.7115000.002135.510.000.00.001-07-20121919.13NaN01-08-2015INDIVIDUALNaN
1590061542543000.03000.02988.2436 months14.26%102.92CC5NaN3 yearsMORTGAGE80800.0Not Verified01-09-2009Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=59006I am seeking to refinance a credit account which I closed with a balance when I rejected the new terms of the cardmember agreement. This closed account is adversely affecting my credit utilization percentage and I would prefer to move it to a fixed-rate loan. I am a software developer who has been in a stable position with the same company since 2004. I am up-to-date on all payments and am seeking only to reduce the interest rate of this debt. Thank you for your consideration.credit_cardRejecting new cardmember agreement775xxTX14.971.001-07-1998013.00.01304740.039.50%23f0.00.03705.003688.853000.00705.000.000.00.001-10-2012111.23NaN01-09-2012INDIVIDUALNaN
2654262321064000.04000.03892.2636 months11.14%131.22BB1Infotrieve, Inc.< 1 yearMORTGAGE60000.0Not Verified01-08-2009Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=65426We currently have one car that is 19 years old and one that is 8 years old. The 19 year old car, which is the car my husband drives to his job at a local university, was just given about a month to live by our mechanic. We've gotten an amazing amount of use out of it but we will need to be sure to get a reliable vehicle before that one gives out. We hope to be able to donate it with some life left in it to a local non-profit. That is what we have done in the past with our old cars. Our mechanic will help us find a used car in great shape for around $10,000. We have saved about half of that but we really need to make a purchase soon. It would be fabulous to get a loan for a lower percentage rate than what our credit union offers. Currently that is probably about 11% for older vehicles. Thanks for considering us.cardjp481xxMI11.080.001-08-199500.00.014024220.068.60%33f0.00.02755.202615.802170.35584.850.000.00.001-06-2011131.22NaN01-05-2016INDIVIDUALNaN
3689262649242300.02300.0589.6136 months13.17%77.69DD2UBS10+ yearsRENT37152.0Verified01-08-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=68926I need a loan to cover moving expenses such as buying new furniture, deposit on the apt etc.movingtee_cee088xxNJ2.260.001-12-1997046.00.0402211.088.40%13f0.00.02796.60643.502300.00496.600.000.00.001-09-201177.78NaN01-05-2016INDIVIDUALNaN
4692512677716000.06000.0500.0036 months8.00%188.02AA3NaN< 1 yearMORTGAGE75000.0Not Verified01-05-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=69251Looking to pay bills with a lower rate and try a new type of lending. Please note my perfect credit history and ability to pay the account. Many Thanks HeatherotherNewOrganic441xxOH16.080.001-12-199410.00.016029797.023.20%39f0.00.06783.75565.315999.99768.7615.000.00.001-05-2011189.36NaN01-05-2011INDIVIDUALNaN
5656402345695000.02650.0495.4936 months11.34%87.19CC2kmex/univision10+ yearsMORTGAGE90000.0Not Verified01-05-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=65640This money would be used to finish a remodeling kitchen project.home_improvementproducer46912xxCA17.250.001-05-199710.00.020069909.051.10%51f0.00.03153.80512.182649.99488.8215.000.00.001-05-201187.83NaN01-04-2015INDIVIDUALNaN
66992427428010000.010000.08790.3336 months13.55%339.60DD4GAP3 yearsRENT100000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=69924Hello friends, I am trying to pay off couple of credit cards which has raised the APR recently, increasing my monthly payments Thanks Sunnycredit_cardTrying to pay off high interest cards941xxCA7.940.001-11-200200.00.011021162.057.70%14f0.00.012225.4110738.0310000.002225.410.000.00.001-04-2011359.55NaN01-02-2016INDIVIDUALNaN
76982827279815000.015000.013138.2036 months8.63%474.42AA5State of Michigan10+ yearsOWN50000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=69828We have owned and operated a year round greenhouse/nursery, organic food processing operation in the the central Michigan area since 1990. Last season we had the opportunity to purchase 12,000 square foot of 4 year old greenhouse space, and we are seeking "investors" who would be willing to help us with reconstruction costs, so we will have the added greenhouse space for organic, sustainable, local food production. We also intend to make equipment upgrades to our commercial processing kitchen.otherBusiness Expansion488xxMI2.590.001-06-197500.00.0405656.027.60%25f0.00.017208.1815033.5015000.002113.3094.880.00.001-08-201138.20NaN01-08-2011INDIVIDUALNaN
828270728264110000.010000.08741.0436 months9.45%320.10BB1Brightstar Corporation2 yearsRENT70000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=282707I'm a successful 25 year old sales rep. This loan is for my education, but not books or a common education, more like a street smart education and experience. I'm practicing how to raise capital/borrow money to create profitable returns. This is a skill I want to master to create wealth. I'm also enjoying the experience as I don't "need" the money. I don't have a mortgage, my income exceeds my expenses and I'm very meticulous about spending. I have good control over myself mentally, spiritually and emotionally, therefore I have good control over my money. My only debt is a car lease and a interest free care credit loan for my lasik (i could have paid cash but why not get interest free financing). My credit card balances are paid in full during their billing cycle - they are used solely to build credit and get points. I don't have school loans as I did my undergrad as a Fulbright Scholar, and MBA's are overrated. So I'm pretty liquid. Even my wife's diamond ring is paid in full, and her parents paid for the wedding. So why borrow if I have money? Again, this is for the experience, I can comfortably afford the loan and interest, but I think this experience will make me better in business. There is bad debt, when you buy liabilities or things that depreciate. This is good debt, the kind that puts money in my pocket building an asset. Thanks for your time, CarloseducationalSocial Entrepreneur600xxIL9.380.001-02-200300.00.0808850.032.30%8f0.00.011466.3910004.7710000.001466.400.000.00.001-02-2011306.86NaN01-02-2011INDIVIDUALNaN
928256927415812000.012000.06475.0036 months13.55%407.52DD4Wachovia Bank< 1 yearMORTGAGE55000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=282569A year ago I purchased my dream home and with that purchase, credit card debt slowly built up. Unfortunately the rates on these cards are through the roof. My plan was to take a year and agressively pay off the balances but rates are so high. When compounding interest enters into the equation I end up only paying off a small percentage of the principal. For the past year I have been paying $400-500 a month towards credit debt and need a solution to compounding interest. The idea of having a fixed rate is exactly what I am looking. Yes, the rates may be higher then traditional lenders but I like the concept of this new type of lending because there is a little more forgiveness towards the borrower. In fact I am a Financial Center Manager with Wachovia Bank but I have run into DTI problems on there grading scale for a loan.credit_cardRefinance credit card debt191xxPA14.990.001-11-1995261.00.012010918.059.00%45f0.00.013861.237479.2912000.001861.230.000.00.001-08-20097355.37NaN01-09-2009INDIVIDUALNaN
idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_lineinq_last_6mthsmths_since_last_delinqmths_since_last_recordopen_accpub_recrevol_balrevol_utiltotal_accinitial_list_statusout_prncpout_prncp_invtotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntnext_pymnt_dlast_credit_pull_dapplication_typepub_rec_bankruptcies
3970728612028486312000.012000.09542.1636 months13.55%407.52DD4Bank2 yearsMORTGAGE130000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=286120These funds will be used to buy an equity position in 40 acres of pine trees. The timber has been valued at $58,000. I will be a 50% partner in the tree farm. I will be putting $8,000 of personal cash towards the deal. This will be the second deal of similar nature for me. I have a great job in the financial industry and am a good candidate for this loan. Due to the volatility in real estate and the stock market, commodities such as timber are solid investments.otherTimber Investment392xxMS8.320.001-10-1999024.0NaN9022044.081.60%21f0.00.014689.6611666.6412000.002670.6718.990.00.001-03-2011434.37NaN01-03-2011INDIVIDUAL0.0
3970828578128577814000.014000.010100.0036 months9.45%448.14BB1Citibank4 yearsRENT42000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=285781I have been trying to pay off a couple of credit card debts and have consolidated them into two loans, but with 19.99% interest rates I feel most of the payments are going to interest. I would prefer a lower interest rate and be done with it in 3 years or less.debt_consolidationFinally Over107xxNY5.310.001-11-1999051.0NaN12011556.024.30%18f0.00.014715.6710616.4314000.00715.670.000.00.001-10-200812028.28NaN01-10-2008INDIVIDUAL0.0
397092857382857324000.04000.03000.0036 months10.39%129.81BB4Wachovia Corp.< 1 yearMORTGAGE53000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=285738I need to pay high interest capital one card so that it would make sense to have a higher payment to pay it off sooner.credit_cardInterest rate too high on credit card917xxCA13.090.001-01-1996144.0NaN1101308.06.90%25f0.00.04383.953287.984000.00383.950.000.00.001-04-20092827.72NaN01-12-2012INDIVIDUAL0.0
397102853862853838000.08000.06525.0036 months8.63%253.03AA5Harland Electric10+ yearsMORTGAGE54080.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=285386loan used to pay off credit cards and pay for home improvementsotherpersonal loan014xxMA10.780.001-11-19981NaNNaN705623.067.70%14f0.00.09045.357377.708000.001045.350.000.00.001-07-20102218.89NaN01-11-2012INDIVIDUAL0.0
3971128463728463012000.012000.08425.0036 months12.29%400.24CC5LLC3 yearsRENT90000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=284637I need this loan at a better rate to consolidate my debts and pay one low monthly installment.credit_cardConsolidate Debts750xxTX8.810.001-10-20011NaNNaN13015486.033.10%13f0.00.012586.968837.0912000.00586.960.000.00.001-10-2008114.04NaN01-09-2008INDIVIDUAL0.0
397122842072842047500.07500.05387.5036 months11.97%249.00CC4SOCIAL SERVICES< 1 yearOTHER19200.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=284207Need load to consolidate debt.debt_consolidationDEBT CONSOLIDATION908xxCA10.940.001-12-20023NaNNaN1206450.070.90%13f0.00.08964.006395.157499.991464.010.000.00.001-03-2011249.00NaN01-05-2016INDIVIDUAL0.0
3971328413628412525000.025000.08933.6036 months9.76%803.87BB2Mayfield City School District7 yearsMORTGAGE70000.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=284136I am trying to secure this loan to increase my investment portfolio. I have between $1000.00 to $1500.00 that I have been putting towards my investments, but with the market like it is, I would like to invest in more companies now and use the $1000.00 to $1500.00 of excess cash I make each month towards this loan. This way, I would have a decent amount of cash to invest in the coming months, and the payemnts on my loan would be under what I am investing per month currently, making it easy to pay back this loan. Of course, I know there is interest on this loan, but I believe that the I can realize gains in the market over the next three years that will be more than the interest I am paying for this loan. I also currently have no outstanding loans other than a morgage payment and have never carried an overdue balance or have had a late payment on any of my credit cards.otherInvesting440xxOH4.920.001-11-19960NaNNaN703114.011.30%13f0.00.028900.649975.2525000.003900.640.000.00.001-01-201145.94NaN01-01-2011INDIVIDUAL0.0
3971428382628382315000.015000.09019.3036 months9.45%480.15BB1US Army10+ yearsRENT62400.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=283826pay off credit card and place some money in the savingsdebt_consolidationdrodo136xxNY6.460.001-11-19952NaNNaN405196.034.60%14f0.00.017332.2410261.0315000.002308.2324.010.00.001-02-2011997.37NaN01-03-2011INDIVIDUAL0.0
3971528370721176520000.020000.04031.2936 months11.34%658.00CC2Jada Beauty1 yearOTHER55000.0Not Verified01-03-2008Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=283707This loan will be used to expand my growing business. We are the first in our state to specialize in Hair Threading and Henna tattoo. We have outgrown our mall location and need to expand to a bigger location to accommodate our growing clientele. This loan will be used to put money down on a bigger location and add new services too like manicures,facials, and pedicures with and Asian twist. I am a experinced licensed professional with over 20 year experince. My financial situation: I am a good candidate for this loan because I have worked very hard to have good credit and paying off my debt is very important to me. I will also be using my own savings in this ventureotherExpanding my growing Business852xxAZ5.192.001-03-1985021.0NaN805482.017.40%28f0.00.06767.461369.235044.601722.860.000.00.001-02-2009658.00NaN01-09-2009INDIVIDUAL0.0
3971628310626454811000.011000.09375.0036 months12.29%366.89CC5Kaiser Permanente4 yearsMORTGAGE36400.0Not Verified01-03-2008Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=283106Want to pay off all credit card debt acquired while in college.debt_consolidationOperation Freedom From Debt925xxCA11.600.001-01-20042NaNNaN6010765.060.50%9f0.00.013130.9811191.1811000.002130.980.000.00.001-09-20102508.69NaN01-10-2015INDIVIDUAL0.0